Brakefield Whitney S, Ammar Nariman, Olusanya Olufunto, Ozdenerol Esra, Thomas Fridtjof, Stewart Altha J, Johnson Karen C, Davis Robert L, Schwartz David L, Shaban-Nejad Arash
University of Tennessee Health Science Center-Oak Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis TN, USA.
The Bredesen Center for Data Science, University of Tennessee, Knoxville. TN, USA.
Stud Health Technol Inform. 2020 Nov 23;275:22-26. doi: 10.3233/SHTI200687.
The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.S. metropolitan region to provide near real-time analysis and dashboarding of ongoing COVID-19 conditions. Our goal is to illuminate associations between SDoH factors and downstream pandemic health outcomes to inform specific policy decisions and public health planning.
新冠疫情正在广泛削弱全球健康和经济，同时对社会弱势群体产生了不成比例的影响。一个有效的疫情监测解决方案必须从健康的社会决定因素（SDoH）的多维度整合中汲取信息，以便在具体情境中为传统流行病学因素提供参考。在本文中，我们描述了一种城市公共卫生观测站（UPHO）模型，我们已在美国一个中型大都市地区实施该模型，以提供对新冠疫情当前状况的近实时分析和数据展示。我们的目标是阐明SDoH因素与下游疫情健康结果之间的关联，为具体的政策决策和公共卫生规划提供依据。
